The field of optical microscopy for biological applications has taken a qualitative leap forward with the technical advances leading to the detection of single particles. In recent years, single particle experiments have become routine in many laboratories using imaging techniques in biology and biophysics, providing new insights into a multitude of biological processes. In most cases, the first step for a quantitative analysis of single particle experiments is the determination of the position of the particle with sub-pixel accuracy in the nanometer range, well below the diffraction limit of light microscopy. For instance, the precise position of fluorescently labeled proteins in consecutive time-lapse images can be used to determine the diffusion properties of specific membrane proteins or to unravel the stepping mechanisms of molecular motors.
In recent years, several super-resolution optical microscopy techniques have been developed that surpass the diffraction limit of light in optical systems (typically about 250 nm). Among these are (fluorescence) photoactivation localization microscopy ((F)PALM), stochastic optical reconstruction microscopy (STORM), and (GSD) ground state depletion microscopy. These techniques are based on the sequential photo-switching of sparse subsets of single fluorophores. They exploit the ability to accurately determine the center of the point spread function (PSF) created by each single point emitter; ultimately, the resolution of the image is determined by the achieved particle localization accuracy. These techniques have become widespread due to their affordability and relatively simple implementation on a conventional total internal reflection fluorescence (TIRF) microscope.
Generally, stochastic optical reconstruction includes three steps: (i) the acquisition of tens of thousands of images of single particles from the sample; (ii) the precise localization of up to a million isolated single emitters; and (iii) the visualization of the super-resolved image reconstructed from the position of detected individual particles. The sequential nature of these steps, together with the high acquisition frame rate and the heaviness of the processing step, usually prevent the user from viewing super-resolution images during image acquisition. As a result, for the routine user it is not possible to access the data prior to post-processing, leading to a tremendous loss of time since the overall acquisition pipeline has to be fragmented.
FIG. 1 illustrates a typical procedure 100 for recording and reconstructing a super-resolution image with a stochastic optical reconstruction technique like PALM microscopy. The procedure 100 involves acquiring the images with a fluorescence microscope (not shown), then post-processing the acquired images according to the following steps. In step 102, a short pulse of visible light activates a subset of fluorophores widely separated far enough to individually resolve each PSF. In step 104, a second laser with a different wavelength is used to excite the active fluorophores until their (irreversible) photobleaching while one or several images 112 are recorded. Steps 102 and 104 are repeated sequentially to activate, then irreversibly photobleach different subsets of fluorophores until the density of imaged fluorophores is high enough for a complete reproduction of the structure of interest (typically a few thousand frames). Once image acquisition is complete, post-processing occurs, starting with the detection of the imaged fluorophores in step 106 on a frame-by-frame basis. Once a possible fluorophore is detected in a particular frame, its location is determined by fitting a Gaussian with a profile similar to the PSF in step 108. Step 108 is repeated for each frame of the acquired data. The reconstructed image in step 110 is obtained by superposition of all the localizations to form a super-resolution image 116. As understood by those of skill in the art, one or more processors and/or processing units may perform steps 106, 108, and 110.
The standard mathematical model used for PSF fitting is a two-dimensional Gaussian function, due to its good performance in terms of localization. Normally acquisition steps 102 and 104 take minutes, while processing steps 106, 108, and 110 may take up to several hours of computation when Gaussian fitting is carried out, since it requires an iterative minimization step, typically a maximum-likelihood estimation (MLE) or non-linear least squares (NLLS). This makes it virtually impossible to quickly evaluate the results obtained in the microscope right after acquisition, and to improve the experimental conditions on-site. Recently, a massively parallel implementation of MLE Gaussian fitting has been proposed. This solution greatly reduced the computation time, but required the use of a dedicated graphics processing unit (GPU) hardware architecture.